Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces
The measurement of plant sap flow has long been a traditional method for quantifying transpiration. However, conventional direct measurement methods are often costly and complex, thereby limiting the widespread application of tree sap flow monitoring techniques. The concept of a Virtual Measurement...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/9/1768 |
| _version_ | 1797580014980431872 |
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| author | Biao Zhang Dongmei Zhang Zhongke Feng Lang Zhang Mingjuan Zhang Renjie Fu Zhichao Wang |
| author_facet | Biao Zhang Dongmei Zhang Zhongke Feng Lang Zhang Mingjuan Zhang Renjie Fu Zhichao Wang |
| author_sort | Biao Zhang |
| collection | DOAJ |
| description | The measurement of plant sap flow has long been a traditional method for quantifying transpiration. However, conventional direct measurement methods are often costly and complex, thereby limiting the widespread application of tree sap flow monitoring techniques. The concept of a Virtual Measurement Instrument (VMI) has emerged in response to this challenge by combining simple instruments with Artificial Intelligence (AI) algorithms to indirectly assess specific measurement objects. This study proposes a tree sap flow estimation method based on environmental factors and AI algorithms. Through the acquisition of environmental factor data and the integration of AI algorithms, we successfully achieved indirect measurement of tree sap flow. Accounting for the time lag response of the flow to environmental factors, we constructed the <i>Magnolia denudata</i> sap flow estimation model using the K-Nearest Neighbor (KNN), Random Forest (RF), Backpropagation Neural Network (BPNN), and Long Short-Term Memory network (LSTM) algorithms. The research results showed that the LSTM model demonstrated greater reliability in predicting sap flow velocity, with R<sup>2</sup> of 0.957, MAE of 0.189, MSE of 0.059, and RMSE of 0.243. The validation of the target tree yielded an R<sup>2</sup> of 0.821 and an error rate of only 4.89% when applying the model. In summary, this sap flow estimation method based on environmental factors and AI provides new insights and has practical value in the field of tree sap flow monitoring. |
| first_indexed | 2024-03-10T22:45:19Z |
| format | Article |
| id | doaj.art-684afe83ac5b4759a68851bf45cb8c06 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T22:45:19Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-684afe83ac5b4759a68851bf45cb8c062023-11-19T10:45:42ZengMDPI AGForests1999-49072023-08-01149176810.3390/f14091768Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green SpacesBiao Zhang0Dongmei Zhang1Zhongke Feng2Lang Zhang3Mingjuan Zhang4Renjie Fu5Zhichao Wang6Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Tsinghua East Road, Beijing 100083, ChinaShanghai Academy of Landscape Architecture Science and Planning, 899 Longwu Road, Shanghai 200433, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Tsinghua East Road, Beijing 100083, ChinaShanghai Academy of Landscape Architecture Science and Planning, 899 Longwu Road, Shanghai 200433, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Tsinghua East Road, Beijing 100083, ChinaShanghai Academy of Landscape Architecture Science and Planning, 899 Longwu Road, Shanghai 200433, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Tsinghua East Road, Beijing 100083, ChinaThe measurement of plant sap flow has long been a traditional method for quantifying transpiration. However, conventional direct measurement methods are often costly and complex, thereby limiting the widespread application of tree sap flow monitoring techniques. The concept of a Virtual Measurement Instrument (VMI) has emerged in response to this challenge by combining simple instruments with Artificial Intelligence (AI) algorithms to indirectly assess specific measurement objects. This study proposes a tree sap flow estimation method based on environmental factors and AI algorithms. Through the acquisition of environmental factor data and the integration of AI algorithms, we successfully achieved indirect measurement of tree sap flow. Accounting for the time lag response of the flow to environmental factors, we constructed the <i>Magnolia denudata</i> sap flow estimation model using the K-Nearest Neighbor (KNN), Random Forest (RF), Backpropagation Neural Network (BPNN), and Long Short-Term Memory network (LSTM) algorithms. The research results showed that the LSTM model demonstrated greater reliability in predicting sap flow velocity, with R<sup>2</sup> of 0.957, MAE of 0.189, MSE of 0.059, and RMSE of 0.243. The validation of the target tree yielded an R<sup>2</sup> of 0.821 and an error rate of only 4.89% when applying the model. In summary, this sap flow estimation method based on environmental factors and AI provides new insights and has practical value in the field of tree sap flow monitoring.https://www.mdpi.com/1999-4907/14/9/1768artificial intelligence algorithmscomputational virtual measurementenvironmental factorssap flowvirtual measuring instrument |
| spellingShingle | Biao Zhang Dongmei Zhang Zhongke Feng Lang Zhang Mingjuan Zhang Renjie Fu Zhichao Wang Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces Forests artificial intelligence algorithms computational virtual measurement environmental factors sap flow virtual measuring instrument |
| title | Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces |
| title_full | Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces |
| title_fullStr | Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces |
| title_full_unstemmed | Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces |
| title_short | Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of <i>Magnolia denudata</i> in Shanghai Urban Green Spaces |
| title_sort | assessment of the potential of indirect measurement for sap flow using environmental factors and artificial intelligence approach a case study of i magnolia denudata i in shanghai urban green spaces |
| topic | artificial intelligence algorithms computational virtual measurement environmental factors sap flow virtual measuring instrument |
| url | https://www.mdpi.com/1999-4907/14/9/1768 |
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